The disconnectivity graph that is generated from the threshold simulations could be viewed as a form of clustering of local energy minima, grouping those that are related by the lowest energy barriers. The results for tetrolic acid demonstrate that there is a link between the basin structure on the crystal energy landscape and geometric features of the crystal structures -in this case, hydrogen bonding motif. The results for TTBI also suggest that crystal density differences explain some of the grouping of local energy minima into the superbasins on the energy landscape. Therefore, we asked the question whether structural descriptors commonly used in machine learning applications could provide a more general geometrical descriptor of structural similar-Table : Lid energies at which transitions are located between γ, α, β, δ and ε in threshold sampling in space group P 1 with four independent molecules in the unit cell (upper right entries). The entries in the lower left are the lowest energy lid at which transitions are found in the simulations in P 2 1 /c and P 1. Energies in italics along the diagonal are the calculated lattice energies of the four structures. All energies are in kJ mol We have taken the GAZCES co-crystal energy landscape in space group P 2 1 /c as an example. This system was chosen for investigation due to the clear structure of the energy landscape and because of the sufficiently large number of structures in each basin from threshold simulations. For analysis, the total energy landscape was divided into three regions: two funnels, each containing an initial structure (polymorph I or II) and the structures outside the two basins. Two common descriptors for crystal systems -SOAP and atom-centered symmetry functions (ACSFs) -were applied with common data featuring methods.