The landscape predicted using the FIT+DMA+GP model using 20% of dimers as training data (Fig. ) gives a faithful reproduction of the FIT+DMA+MP2 landscape, including the relative stabilities of the three observed crystal structures. Only the absolute lattice energies are slightly underestimated with this model, for which only 2,504 of the 12,522 unique dimers have been calculated using MP2. As with oxalic acid, the systematic error is absent at higher training fractions. In both cases the prediction errors with the FIT+DMA+GP models are comparable to previous work on machine learning lattice energies for CSP and highlight the target landscape can be accurately resolved at a significant reduction in computational cost.