The ever fasting development of genetic techniques such as second generation sequencing offers unique opportunities for ecological researchers to study the spatial structure of populations and how they are influenced by landscape features. The creation of such kind of data namely samples of genotyped individuals with known spatial locations, is not paralleled by the application of landscape genetic methods to analyse them. Though a standard approach to study the effect of landscape features using resistance matrices combined with a least-cost approach and compare these with genetic distances is developed, we see a lack of publications applying this approach. Our explanation for such a lack of publications is partly due to the required expertise which encompasses the integration and data exchange between three specialised type of software packages (population genetic, geographic information systems and statistical) to analyse the data. We present a newly developed R package that incorporates all necessary steps into a single framework and discuss the performance of the approach under different landscape scenarios. Next to sample size and number of used markers it turns out that the geometry of the landscape and sample sites is important for the performance of the approach. Finally we present how a simulation approach can be used to test the performance in a given landscape scenario before the actual study is undertaken.