Labelling outliers and finding outliers are two completely different questions. There are lots of statitistical methods to determine outliers (Pierce, Grubbs, 3 sigma, box and whisker plots, ect) all of which vary and disagree. You can use the Analyze > Screening > Explore Outliers tool for this.
That said, I always put in a word of caution around outliers. For you a lot is really only an outlier if it experienced a processing different from the rest of your lots. Data shouldn't be thrown away just b/c it makes your "fit bad" or it "looks high" or it makes my "P-value significant".