Due to its general nature, the SOAP kernel reports on intra-molecular correlations (essentially, the geometry of each isomer) as well as on the inter-molecular correlations (crystal packing and intermolecular contacts) between structures. The dual nature of the information encoded in SOAP-REMatch similarity leads to an increased intrinsic dimensionality of the landscape (as revealed by the decay of the eigenvalues of the kernel similarity matrix, shown in the supporting information) compared to that observed for single landscape analysis, where all crystal structures contain the same molecule. This higher effective dimensionality makes the choice of parameters of the kernel and of the sketch-map projection less straightforward. Previously, we found that using the hyperparameters which led to good lattice energy predictions from a Gaussian Process regression also led to insightful sketch-maps, although alternative choices of the environment cutoff distance emphasized different features in the crystal structures (close contacts vs long-range packing). The heterogeneous nature of the multi-landscape comparison exacerbates the dependence of the appearance of the map on the parameters of the kernel. This made it necessary to optimize the parameters in a trial-and-error fashion, which clearly makes the machine-learning analysis more biased than in the single landscape case.