Orbital field matrix (OFM) descriptors were developed with an emphasis on atomic orbitals for representing material structures in datasets of multi-element compounds. The descriptors were based on atomic valence shell electrons and their coordination. In addition to original OFM and OFM1 which is OFM with a column representing information on the center atom, in this work, we present another version, named OFM0, which is OFM1 without information on atomic distances, for predicting the properties of unoptimized structures. We focus on formation energy and phase stability of crystalline systems, while the atomization energy is examined for molecules. With the emphasis on the ability to identify materials with similar properties, here, the applicabilities of OFM, OFM1, and OFM0 are systematically examined with decision tree (DT) regression, random forest (RF) regression, and kernel ridge regression (KRR). We show that the family of OFM descriptors are highly capable to build predictive models for the properties of solids and molecules. The accuracy of a DT and a forest of trees (RF) is comparable to that of the KRR models. The KRR with a Laplacian kernel estimated by OFM1 yields the most accurate predictions, with the formation energy, phase stability, and atomization energy having mean absolute errors (MAEs) of 0.072 eV/atom, 0.059 eV/atom, and 6.74 kcal/mol, respectively. The OFM0 without atomic distances also yields acceptable predictions with respective MAEs of 0.090 eV/atom, 0.069 eV/atom, and 7.77 kcal/mol. The results imply that our descriptors are highly useful to find similar materials.