Structural descriptors are required to convert the atomistic structure of each predicted crystal structure into a suitable input for the statistical machine learning method. Cartesian coordinates are not a suitable choice because they are not invariant under translation, rotation and reflection of the whole system. Among available atomic descriptors which satisfy these requirements, we use atom-centered symmetry functions to describe the local (radial and angular) environment of each atom. We choose these descriptors of atomic local environments because of their success in the development of machine learned force fields and previous machine learning applications to energy model improvement for molecular crystals. We used the recent modification to symmetry function descriptors that separates element pairs (for radial functions) and triplets (for angular functions) to provide a better resolution of the atomic environment description. 32 equispaced Gaussians were used to describe the radial environment in a cut-off sphere of radius 9.3 Å around each atom. For the angular environment, a cut-off radius of 6.27 Å was used, taking 8 equispaced radial Gaussians and 8 angular ones (64 in total). Thus, for a system with N E elements, there are N E × 32 radial and N E (N E + 1)/2 × 64 angular symmetry functions for each atom.