Density functional theory (DFT) is the most widely used accurate method for predicting material properties, but its practicality is limited when used on systems with a large number of atoms or molecules. An example of such systems are molecular crystals as their unit cells usually have tens or hundreds of individual atoms. Let us consider a situation in which we have many known materials. If we want to identify certain materials which exhibit only one specific property, experimental research with such a goal often represents a technical problem. A large amount of experiments required to identify such materials can be time consuming and costly, in the end even unprofitable. Practical solution for such a problem are computer simulations, but DFT calculations of complex structures could also turn out to be impractical which is the main reason for developing alternative methods, like machine learning potentials. Lately, they have proven to be a good substitute. Calculations with machine learning potentials are much faster than DFT and their accuracy can be comparable. Certain potentials are constructed with a focus on a certain classes of materials, because complexity of such potentials grows with the number of different elements in a system. In this thesis, our focus is centered on organic crystals consisting only of C, H, N and O atoms. Such constraints make material property screening much faster and cost effective. Specifically, the main goal is to calculate elastic tensors for such crystals and identify those which could exhibit the property of negative linear compressibility (NLC). At the very end, several such results are verified by a DFT calculation.