The material space for catalyst discovery is expansive. Volcano curves are traditionally employed to provide physical insights into optimal catalyst characteristics for new material selection. Their generation lies on a single descriptor picked using expert knowledge. Here we present DescMAP, a Python-based software, to automate the selection of descriptors, the generation of volcano maps, and the identification of active sites for structure-sensitive reactions. We consider traditional energy-based and geometric descriptors for structure-sensitive reactions. DescMAP is integrated with the Virtual Kinetic Laboratory (VLab) to provide multiple functionalities. It inputs spreadsheets or template files for flexibility and outputs interactive graphs for post-processing. We demonstrate its features using the non-oxidative dehydrogenation of ethane to ethylene over (111) closed-packed surfaces and the methane total oxidation over various Pt facets. It can be easily applied to other complex chemistries and achieves quick screening of potential catalysts. Program summaryProgram title: DescMAPCPC Library link to program files:https://doi.org/10.17632/g399b3xyfy.1Developer's repository link:https://github.com/VlachosGroup/DescriptorMapCode Ocean Capsule:https://codeocean.com/capsule/7598436Licensing provisions: MIT licenseProgramming language: PythonExternal routines: pMuTT, NumPy, Pandas, Matplotlib, Plotly, Scikit-learn, ScipyNature of problem: Screening potential catalysts via microkinetic modeling and generating volcano curves is time-consuming. An automated tool to accelerate this process is lacking.Solution method: Python package with descriptor selection that automates descriptor-based microkinetic modeling. Interactive volcano curves generated for post-analysis.